RIS@QUB
The vision of Reconfigurable and Intelligent Systems @ Queen’s University Belfast is to pioneer the future computing systems that deliver unprecedented levels of efficiency, adaptability, and trustworthiness. By combining advances in artificial intelligence, programmable hardware, computer architecture, and embedded systems, we aim to create sustainable computing technologies capable of powering future edge, autonomous, and cyber-physical systems. Our research seeks to transform how intelligent systems are built, enabling them to learn, adapt, and operate reliably in dynamic and resource-constrained environments.
Research Themes
| ⚡ Approximate Computing - Error-resilient architectures - Quality-energy-performance trade-offs - Sustainable computing - Fault-tolerant edge systems | 🔧 Reconfigurable Computing and FPGA Acceleration - FPGA-based AI accelerators - Automated accelerator generation - High-performance embedded architectures - Design automation methodologies |
| 🚀 Low SWaP-C Embedded Systems - Size, Weight, Power and Cost constrained platforms - Autonomous vehicles and robotics - Edge intelligence - Real-time embedded computing | 🧠 Efficient and Trustworthy AI Systems - Hardware acceleration for AI and machine learning - Energy-efficient AI deployment at the edge - Trustworthy and resilient AI systems - AI for autonomous platforms |
Openning Positions
Accepting Outstanding candidates for PhD all year round in topics of Approximate Computing:
- Efficient & Resilient Reconfigurable ML/AI Accelerators
- AI-Assisted Electronic Design Automations
Accepting Postdoctoral Fellowships - Marie Skłodowska-Curie Actions application all year round. It is due by 9th September 2026 this year.
Please email your CV, transcripts, and a brief statement of research interests to YUN dot WU at QUB dot AC dot UK. You could also monitor official PhD advertisements via: PhD Opportunities at QUB with my name.
Chinese Students Specific Funding Opportunities (Cycling every year)
Opened around mid-November, closed by mid-January of the next year
Queen’s University/CSC PhD Scholarships
Selcted Publications
Energy Efficient Reconfigurable Accelerator for Monocular Underwater Image Enhancement
Joint Undervolting and Overclocking Power Scaling Approximation on FPGAs
Mixed Precision ℓ₁ Solver for Compressive Depth Reconstruction: An ADMM Case Study
Approximate LASSO Model Predictive Control for Resource-Constrained Systems
Architectural Synthesis of Multi-SIMD Dataflow Accelerators for FPGA
Runtime Support for Adaptive Power Capping on Heterogeneous SoCs